Abstract
Genetic Programming (GP), a heuristic optimisation technique based on the theory of Genetic Algorithms (GAs), is a method successfully used to identify non-linear model structures by analysing a system’s measured signals. Mostly, it is used as an offline tool that means that structural analysis is done after collecting all available identification data. In this paper, we propose an enhanced on-line GP approach that is able to adapt its behaviour to new observations while the GP process is executed. Furthermore, an approach using GP for online Fault Diagnosis (FD) is described, and finally test results using measurement data of NOx emissions of a BMW diesel engine are discussed.
Original language | English |
---|---|
Pages (from-to) | 255-270 |
Number of pages | 16 |
Journal | International Journal of Intelligent Systems Technologies and Applications |
Volume | 2 |
Issue number | 2-3 |
DOIs | |
Publication status | Published - 2007 |
Keywords
- data driven model identification
- fault diagnosis
- FD
- genetic programming
- GP
- machine learning
- online modelling
- self-adaption